85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
import pandas as pd
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Dense
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from sklearn.metrics import accuracy_score, precision_recall_fscore_support, mean_squared_error
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from sacred import Experiment
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from sacred.observers import MongoObserver, FileStorageObserver
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from math import sqrt
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ex = Experiment('464979')
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
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ex.observers.append(FileStorageObserver('sacred_runs'))
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@ex.config
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def my_config():
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epochs = 10
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batch_size = 32
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@ex.automain
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def run_experiment(epochs, batch_size, _run):
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train_data = pd.read_csv('beer_reviews_train.csv')
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X_train = train_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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y_train = train_data['review_overall']
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tokenizer = Tokenizer(num_words=10000)
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tokenizer.fit_on_texts(X_train)
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X_train_seq = tokenizer.texts_to_sequences(X_train)
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X_train_pad = pad_sequences(X_train_seq, maxlen=100)
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model = Sequential([
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Embedding(input_dim=10000, output_dim=16, input_length=100),
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GlobalAveragePooling1D(),
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Dense(16, activation='relu'),
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Dense(1, activation='sigmoid')
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])
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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model.fit(X_train_pad, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.1)
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model.save('beer_review_sentiment_model.keras')
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_run.add_artifact('beer_review_model.h5')
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test_data = pd.read_csv('beer_reviews_test.csv')
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X_test = test_data[['review_aroma', 'review_appearance', 'review_palate', 'review_taste']]
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y_test = test_data['review_overall']
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tokenizer = Tokenizer(num_words=10000)
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tokenizer.fit_on_texts(X_test)
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X_test_text = X_test.astype(str).agg(' '.join, axis=1)
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X_test_seq = tokenizer.texts_to_sequences(X_test_text)
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X_test_pad = pad_sequences(X_test_seq, maxlen=100)
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predictions = model.predict(X_test_pad)
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if len(predictions.shape) > 1:
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predictions = predictions[:, 0]
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results = pd.DataFrame({'Predictions': predictions, 'Actual': y_test})
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results.to_csv('beer_review_sentiment_predictions.csv', index=False)
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y_pred = results['Predictions']
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y_test = results['Actual']
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y_test_binary = (y_test >= 3).astype(int)
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accuracy = accuracy_score(y_test_binary, y_pred.round())
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precision, recall, f1, _ = precision_recall_fscore_support(y_test_binary, y_pred.round(), average='micro')
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rmse = sqrt(mean_squared_error(y_test, y_pred))
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print(f'Accuracy: {accuracy}')
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print(f'Micro-avg Precision: {precision}')
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print(f'Micro-avg Recall: {recall}')
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print(f'F1 Score: {f1}')
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print(f'RMSE: {rmse}')
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_run.add_resource('./beer_reviews_train.csv')
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_run.add_resource('./beer_reviews_test.csv')
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return accuracy
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